Objective To develop and validate a novel comorbidity score (multisource comorbidity score (MCS)) predictive of mortality, hospital admissions and healthcare costs using multiple source information from the administrative Italian National Health System (NHS) databases. Methods An index of 34 variables (measured from inpatient diagnoses and outpatient drug prescriptions within 2 years before baseline) independently predicting 1-year mortality in a sample of 500 000 individuals aged 50 years or older randomly selected from the NHS beneficiaries of the Italian region of Lombardy (training set) was developed. The corresponding weights were assigned from the regression coefficients of a Weibull survival model. MCS performance was evaluated by using an internal (ie, another sample of 500 000 NHS beneficiaries from Lombardy) and three external (each consisting of 500 000 NHS beneficiaries from Emilia-Romagna, Lazio and Sicily) validation sets. Discriminant power and net reclassification improvement were used to compare MCS performance with that of other comorbidity scores. MCS ability to predict secondary health outcomes (ie, hospital admissions and costs) was also investigated. Results Primary and secondary outcomes progressively increased with increasing MCS value. MCS improved the net 1-year mortality reclassification from 27% (with respect to the Chronic Disease Score) to 69% (with respect to the Elixhauser Index). MCS discrimination performance was similar in the four regions of Italy we tested, the area under the receiver operating characteristic curves (95% CI) being 0.78 (0.77 to 0.79) in Lombardy, 0.78 (0.77 to 0.79) in Emilia-Romagna, 0.77 (0.76 to 0.78) in Lazio and 0.78 (0.77 to 0.79) in Sicily. Conclusion MCS seems better than conventional scores for predicting health outcomes, at least in the general population from Italy. This may offer an improved tool for risk adjustment, policy planning and identifying patients in need of a focused treatment approach in the everyday medical practice.

Cruise passengers’ behaviour at the destination is a rather poorly investigated phenomenon. The single exit/entry point and the relatively brief visiting time, which characterize cruise passengers’ experience at their destinations, make the use of GPS technology particularly suitable for the analysis of such a relevant phenomenon. The aim of this research is to propose a general framework for collecting and analysing GPS tracking data relating to cruise passengers’ behaviour at their destination. The main prerequisites and research stages for the implementation of surveys on cruise passengers will be described and a set of tools and measures for the analysis of GPS tracking data will be proposed, together with their potential applications. As examples of case studies, two surveys performed in the ports of Palermo and Dubrovnik will be described and the main results of the collated information will be presented. Improving our understanding of the behaviour of cruise passengers at their destination is particularly relevant for the management of tourism destinations, given the challenges that this growing phenomenon is posing for many port destinations in the Mediterranean.

Many studies have investigated seasonality in tourism in terms of its causes and impacts, from both theoretical and applied perspectives. However, the definition of seasonality and, most of all, its application and measurement have not received the same degree of attention. In recognizing that seasonality has two main facets – pattern and amplitude – this paper critically examines the main seasonality measures proposed in the tourism literature, classifying them according to their properties and their most appropriate use. The framework then proposed is used to analyse a standard and comparable efficiency measure, such as the bed occupancy rate of Sicilian accommodation establishments, placing particular attention on its seasonality.